Statistical Thinking to Improve Quality

This posting illustrates the use of model building to study cause and effect and reduce common-cause variation.  One approach to model building is to build a model such as a regression model based on either results from an experimental design or observed process data.  Another approach illustrated in this posting is to construct a simulation model based on the system flow chart or process map.    One application of a simulation model is to predict flow times or service times for complex systems.   In service or health system applications customer service or wait times could be useful quality measures.   One uses the simulation model by varying input variables such as the number of servers to predict their effect on customer service times.

Davies (2007) describes a case study involving the treatment of minor injuries and medical problems in an emergency department in England.   Receptionists route arriving patients with minor injuries or medical conditions are routed to the “Minors” department.   The standard processing procedure has receptionists in the Minors department assign patients to a queue for triage nurses who assess the patient condition and needs.   Then the triage nurse routes the patients to a doctor or nurse for treatment.   The nurses are qualified to assess and treat minor injuries but not to handle minor medical conditions which are handled by doctors.   These nurses are Emergency Nurse Practitioners (EPNs).  Call this procedure “See” and “Treat”.   The UK national health service recommended that emergency departments skip the triage nurse step.   The health service recommended that receptionists route patients to a doctor or ENP for diagnosis and treatment.  Call this procedure “See & Treat”.   The intent was to reduce patient system time by eliminating a step and its associated queuing time.   The following figure depicts the “See & Treat” patient flow.

Davies describes a simulation model for comparing the two procedures.   This model represents the processing of individual patients, their waiting times, and individual task processing times.   Inputs to the model would include distributions for task times, distributions for times between patient arrivals, and the numbers of doctors and EPNs.  The following figure presents some of the simulation results.   The new procedure “See & Treat” that eliminates the triage step gives the lowest system time.

References

1. Davies, R. (2007). "See and Treat" or "See" and "Treat" in an Emergency Department. 2007 Winter Simulation Conference. Washington, DC.
   

 

Posted by Gordon M. Clark at 06:54 PM | Permalink | Comments (0)

This post starts a series of posts to present the use of Statistical Thinking Tools in applying Statistical Thinking.   The Statistical Thinking Tool illustrated by this example is a flowchart.   We can have flowcharts for processes having service time objectives as well as processes processes producing a physical product.  Jeffries and Sells (2004) present this example and describe the use of “statistical tools” to meet company service time objectives.   We regard their use of statistical tools as an application of Statistical Thinking.

A Midwest manufacturing firm processes orders for its 6 manufacturing plants and 12 warehouses.   Originally, each plant and warehouse had its own order processing service staffed by a total of 36 customer service representatives.  To improve customer service and reduce costs, the company president directed a team to develop a centralized customer service center located at corporate headquarters.   The president made this decision after the team surveyed customers and found that they were adamant that they did not want to wait for a customer service representative to answer a phone call and they were not very interested in personalized service provided by a plant or warehouse representative.

The team established a goal where 95% of incoming calls would wait less than 2 minutes for a customer service representative.   The team acquired an Automatic Call Distribution (ACD) system to route customer calls to customer service representatives.  The call center would operate from 7:00 am to 7:30 pm Central Time.   The following figure gives a flowchart specifying the process of answering incoming customer calls.

The team collected data giving the distributions of incoming calls by time of day and the service times of the customer service representatives to answer the calls.  Recording and analyzing data for individual steps in the process flow chart is an example of disaggregation.   Classifying and analyzing data by a factor such as time of the day is an example of stratification.

The customer service center staffing levels by hour of the day is a crucial system design parameter.   Wait times will be long without adequate staff.  On two occasions in the past two months, I have had to wait more than an hour for technical service support personnel to answer my calls.   I know that this happens because the companies involved have allocated inadequate staffing to handle the incoming calls.

The team developed staffing levels throughout the day using a simulation of the process represented by the figure above.   Constructing a simulation requires a flowchart.  Refer to Jeffries and Sells (2004) for additional details.

The next post will illustrate the use of a flowchart for a process producing a physical product.

References

1. Jeffries, R. D. and P. R. Sells (2004). Managing Customer Service Using Statistical Tools: A Case Study. Annual Quality Congress Proceedings.
   

Posted by Gordon M. Clark at 09:32 PM | Permalink | Comments (0)